Hadas Kogan

Vanishing points estimation by self-similarity

This paper presents a novel self-similarity based approach for the problem of vanishing point estimation in man-made scenes. A vanishing point (VP) is the convergence point of a pencil (a concurrent line set), that is a perspective projection of a corresponding parallel line set in the scene. Unlike traditional VP detection that relies on extraction and grouping of individual straight lines, our approach detects entire pencils based on a property of 1D affine-similarity between parallel cross-sections of a pencil. Our approach is not limited to real pencils. Under some conditions (normally met in man-made scenes), our method can detect pencils made of virtual lines passing through similar image features, and hence can detect VPs from repeating patterns that do not contain straight edges. We demonstrate that detecting entire pencils rather than individual lines improves the detection robustness in that it improves VP detection in challenging conditions, such as very-low resolution or weak edges, and simultaneously reduces VP false-detection rate when only a small number of lines are detectable.

A robust similarity measure for automatic inspection

We introduce a new similarity measure that is insensitive to sub-pixel misregistration. The proposed measure is essential in some differences detection scenarios. For example, in a setting where a digital reference is compared to an image, where the imaging process introduces deformations that appear as non constant misregistration between the two images. Our goal is to ignore image differences that result from misregistration and detect only the true, albeit minute, defects. In order to define a misregistration insensitive similarity, we argue that a similarity measure must respect convex combinations. We show that the well known SSIM [1] does not hold this property and propose a modified version of SSIM that respects convex combinations. We then use this measure to define Sub-Pixel misregistration aware SSIM (SPSSIM).